Improved Genetic Algorithm Based k-means Cluster for Optimized Clustering

The Human Freedom Index (HFI) is an annual evaluation that measures a variety of factors, such as the rule of law, security, religion, expression, and regulation, to determine the degree of human freedom. On the basis of these considerations, relationships between social and economic factors have been developed. Several agents of intelligent software are frequently utilizing clustering techniques in filtering, extracting, categorizing materials that are already present on the World Wide Web since clustering approaches deal with the enormous volume of information. Dataset involved in this research is HFI at 2022 which involves 3464 observations and 141 features. The great sensitivity of the initial cluster centers, which may cause the K-Means method to become trapped in the local optimum is one of the major issues. The proposed work Genetic Algorithm (GA) using density method to address the drawbacks of K means cluster includes clustering numbers, as well as local optimization. In contrast to the initial cluster centroids that are chosen at random using the Improved Genetic Based K Means (IGBKM) clustering technique introduced for utilized chromosomes in creating cluster centroids. The KMeans clusters have commenced with the best cluster centers suggested using GA which maximize the fitness functions. In comparison to traditional k-means clustering technique, the results reveal in improving the K Means performance through genetic based by sensible selection for initiating the cluster centroids.